{"id":"W4388447054","doi":"10.21203/rs.3.rs-3504340/v1","title":"Classifying Emergency Patients into Fast-Track and Complex Cases Using Machine Learning","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Triage; Gradient boosting; Logistic regression; Artificial intelligence; Random forest; Machine learning; Perceptron; Specialty; Computer science; Boosting (machine learning); Emergency department; Fast track; Medical emergency; Medicine; Artificial neural network; Family medicine; Surgery; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002139449,0.0006595505,0.000581747,0.004159373,0.0003744892,0.001802264,0.0008195568,0.000868539,0.00240229],"category_scores_gemma":[0.009158431,0.0001313518,0.001113463,0.001633444,0.0002064825,0.001213422,0.0006144564,0.0009193097,0.0009678841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006501203,"about_ca_system_score_gemma":0.001289897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003120437,"about_ca_topic_score_gemma":0.003030816,"domain_scores_codex":[0.9987941,0.0004332026,0.0001928149,0.0002079124,0.0001947489,0.0001772561],"domain_scores_gemma":[0.9960174,0.002334851,0.0005513105,0.0001517425,0.0006966818,0.0002479823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006344168,0.001272709,0.5693735,0.0003699149,0.0002937326,0.000551526,0.0002529271,0.05249657,0.001634016,0.001339378,0.01343619,0.3583452],"study_design_scores_gemma":[0.00004434574,0.0004322374,0.1115133,0.0002472176,0.0001698474,0.0004717445,0.0008609941,0.8765824,0.002280896,0.00454874,0.00277867,0.00006953262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8218062,0.002351016,0.1591288,0.003896213,0.0005587402,0.000723723,0.003686145,0.001429768,0.006419335],"genre_scores_gemma":[0.9411386,0.0004905649,0.05288779,0.0002597605,0.0002314859,0.0001395613,0.003873128,0.00002316037,0.0009559751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004159373,"threshold_uncertainty_score":0.01131463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2587317497634068,"score_gpt":0.4751849608987774,"score_spread":0.2164532111353706,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}